Method for estimating scan parameters from tomographic data

ABSTRACT

The methods and systems of the present invention is an algorithm which estimates motion inside objects that change during the scan. The algorithm is flexible and can be used for solving the misalignment correction problem and, more generally, for finding scan parameters that are not accurately known. The algorithm is based on Local Tomography so it is faster and is not limited to a source trajectory for which accurate and efficient inversion formulas exist.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to co-pending U.S. patent applicationSer. No. 13/152,997, entitled “An Algorithm for Motion Estimation fromthe Tomographic Data”, filed Jun. 3, 2011, which claims priority to U.S.Provisional Patent Application No. 61/351,614 filed on Jun. 4, 2010, thecontents of which are herein incorporated in their entirety.

FIELD OF INVENTION

This invention relates to tomography and, in particular, to methods,systems and devices for tomography with motion estimation forreconstructing objects that change during the scan and misalignmentcorrection for reconstructing objects when some scan parameters are notaccurately known.

BACKGROUND OF THE INVENTION

Cardiac and, more generally, dynamic imaging is one of the topchallenges facing modern computed tomography. When the object beingscanned changes during data acquisition the classic tomographicreconstruction theory does not apply. In cardiac computed tomographythere are two major groups of approaches for dealing with this issue.One is based on gating, i.e., selecting the computed tomography datawhich correspond to a fixed cardiac phase, and then using mostly thatdata for image reconstruction. The second approach, known as motioncompensation, is based on incorporating a motion model into areconstruction algorithm. Motion compensation algorithms are preferable,because they use all data and have the potential to provide good imagequality with reduced x-ray dose. The main difficulty of using suchalgorithms is that the motion model needs to be known. There are motionestimation algorithms available, but significant research still needs tobe done to improve efficiency, accuracy, and stability with respect tonoise, flexibility, and the like.

The methods and systems of the present invention solve the problemsassociated with the prior art using a novel approach to motionestimation, which is based on local tomography (LT). The ultimate goalis a robust algorithm which can reconstruct objects that change duringthe scan. Since there is no formula that recovers the object f andmotion function ψ from the tomographic data, the most realistic approachto finding f and ψ is via iterations. On the other hand, recovering bothof them at the same time would result in an iterative problem of aprohibitively large size.

The best approach is to decouple the two tasks, motion estimation andmotion compensation, as much as possible. Not all methods achieve thisgoal. For example, when finding ψ using registration, one uses theimages of f at different times. In other words, finding ψ depends on theknowledge of f. This has undesirable consequences. When motion is notknown, f is reconstructed with significant artifacts, making subsequentregistration unreliable and inaccurate. In contrast, LT is an idealcandidate for decoupling. LT does not reconstruct pointwise values of f,but rather a gradient-like image of f with edges enhanced. Thus the onlyinformative feature of LT is the location of edges.

SUMMARY OF INVENTION

A first objective is an algorithm for motion estimation from tomographicdata that can be used for improved image reconstruction from CT data inthe case when there is motion in the object (e.g., cardiac motion orbreathing motion) during the scan.

A second objective is an algorithm for estimating scan parameters fromtomographic data that can be used for correcting for the imperfectionsin the x-ray source trajectory or other scan parameter such asmisalignment and the like.

The methods and systems of the present invention show that when anygiven edge is seen from the data from two or more source positions, thenin the case of incorrectly known motion, the single edge “spreads” andbecomes a double edge. As a result, the image looks more cluttered. Asolution to the problems associated with the prior art is to iterativelyimprove the motion model so that image clutter is minimized. The presentinvention provides an empiric measure of clutter, referred to “edgeentropy.” Note that the word “entropy” in the name is largely symbolic,since the present invention does not establish any properties thatconventional entropy must possess. In the present invention motionestimation is completely independent of the knowledge of f, and thedesired decoupling is achieved. No knowledge of f is required. The onlything needed is that f possess a sufficient number of edges, which istrue for practically all f occurring in medical imaging. The use of LThas other benefits as well. (1) LT is very fast. First, it does notrequire global filtering. Second, backprojection is greatly simplified,since there is no need to compute complicated weights that are mandatoryfor quasi-exact motion compensating inversion formulas. The weightscompensate for variable length of illumination for every voxel in animage. Clearly, high reconstruction speed is critically important foriterative-based motion estimation. (2) LT uses only local data; hence itis not sensitive to data truncation. (3) LT is very flexible and can beused with practically any source trajectory.

Let us mention some other attractive features of the present invention.First, it is local in time. Motion estimation is done inside areasonably short time window, e.g., not much longer than the length of ashort scan. This eliminates the need for making the periodicityassumption as described in S. Bonnet. A. Koenig, S. Roux, P. Hugonnard,R. Guillemaud, and P. Grangeat, Dynamic X-ray computed tomography, Proc.IEEE, 91 (2003), pp. 1574-1587, which frequently holds onlyapproximately. Second, the approach is fairly general and can be usedfor several types of motion, e.g. cardiac, breathing, etc. Finally, withsimple modifications the approach can be applied to solving otherpractically important problems. As an example we show how to solve amisalignment correction problem for a distorted circular scan. A similariterative algorithm, which is based on the Feldkamp inversion formula,is described in Y. Kyriakou, et al., Simultaneous misalignmentcorrection for approximate circular cone-beam computed tomography, Phys.Med. Biol. 53 (2008), pp. 6267-6289.

Since the algorithm of the present invention is based on LT, it isfaster and is not limited to a source trajectory for which accurate andefficient inversion formulas exist. As before, estimation of the unknownsource trajectory is completely decoupled from finding f, so for thelatter purpose one can use any algorithm. For example, when the data aretruncated, one might want to use an iterative reconstruction algorithm.If the two problems are coupled, using an iterative algorithm forfinding f inside an iterative algorithm for estimating the sourcetrajectory is prohibitively slow.

Further objects and advantages of this invention will be apparent fromthe following detailed description of preferred embodiments which areillustrated schematically in the accompanying drawing's.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 a shows x₁x₂-cross-sections of the phantom at time correspondingto view 101.

FIG. 1 b shows x₁x₂-cross-sections of the phantom at time correspondingto view 900.

FIG. 1 c shows x₁x₂-cross-sections of the phantom at time correspondingto view 501.

FIG. 2 a shows x₁x₃-cross-sections of the phantom at time correspondingto view 101.

FIG. 2 b shows x₂x₃-cross-sections of the phantom at time correspondingto view 900.

FIG. 2 c shows x₂x₃-cross-sections of the phantom at time correspondingto view 501.

FIG. 3 a shows x₂x₃-cross-sections of the phantom at time correspondingto view 101.

FIG. 3 b shows x₂x₃-cross-sections of the phantom at time correspondingto view 900.

FIG. 3 c shows x₂x₃-cross-sections of the phantom at time correspondingto view 501.

FIG. 4 a shows a density plot of Bf at the beginning of iterations whenzero motion is assumed for x₁x₂-cross-section through the center of thegrid.

FIG. 4 b shows images of the bright spots corresponding to density plotsshown in FIG. 4 a.

FIG. 4 c shows a density plot of Bf at the beginning of iterations whenzero motion is assumed for x₁x₃-cross-section through the center of thegrid.

FIG. 4 d shows images of the bright spots corresponding to density plotsshown in FIG. 4 c.

FIG. 4 e shows a density plot of Bf at the beginning of iterations whenzero motion is assumed for x₂x₃-cross-section through the center of thegrid.

FIG. 4 f shows images of the bright spots corresponding to density plotsshown in FIG. 4 c.

FIG. 5 a shows the a density plot of Bf at the end of iterations forcross-section x₁x₂ through the center of the grid.

FIG. 5 b shows images of bright spots corresponding to the density plotshown in FIG. 5 a.

FIG. 5 c shows the a density plot of Bf at the end of iterations forcross-section x₁x₂ through the center of the grid.

FIG. 5 d shows images of bright spots corresponding to the density plotshown in FIG. 5 c.

FIG. 5 e shows the a density plot of Bf at the end of iterations forcross-section x₁x₃ through the center of the grid.

FIG. 5 f shows images of bright spots corresponding to the density plotshown in FIG. 5 c.

FIG. 6 a shows a density plot of Bf at the beginning of iterations whena pure circular trajectory is assumed for cross-section x₁x₂ through thecenter of the grid.

FIG. 6 b shows density plots of Bf at the end of iterationscorresponding to FIG. 6 a.

FIG. 6 c shows a density plot of Bf at the beginning of iterations whena pure circular trajectory is assumed for cross-section x₁x₃ through thecenter of the grid.

FIG. 6 d shows density plots of Bf at the end of iterationscorresponding to FIG. 6 c.

FIG. 6 e shows a density plot of Bf at the beginning of iterations whena pure circular trajectory is assumed for cross-section x₂x₃ through thecenter of the grid.

FIG. 6 f shows density plots of Bf at the end of iterationscorresponding to FIG. 6 c.

FIG. 7 a shows a density plot of Bf at the beginning of iterations basedon ∂²/∂q² for cross-section x₁x₂ through the center of the grid.

FIG. 7 b shows the results of bright pixel detection using the LTfunction shown in FIG. 7 a.

FIG. 7 c shows the results of bright pixel detection using the LTfunction based on the filter according to the present invention.

FIG. 7 d shows a density plot of Bf at the beginning of iterations basedon ∂²/∂q² for cross-section x₁x₃ through the center of the grid.

FIG. 7 e shows the results of bright pixel detection using the LTfunction shown in FIG. 7 d.

FIG. 7 f shows the results of bright pixel detection using the LTfunction based on the filter according to the present invention.

FIG. 7 g shows a density plot of Bf at the beginning of iterations basedon ∂²/∂q² for cross-section x₁x₂ through the center of the grid.

FIG. 7 h shows the results of bright pixel detection using the LTfunction shown in FIG. 7 g.

FIG. 7 i shows the results of bright pixel detection using the LTfunction based on the filter according to the present invention.

FIG. 8 a shows a density plot of Bf at the end of iterations based onthe filter of present invention for cross-section x₁x₂ through thecenter of the grid.

FIG. 8 b shows the results of bright pixel detection using the LTfunction shown in FIG. 8 a.

FIG. 8 c shows the results of bright pixel detection using the LTfunction based on the filter according to the present invention.

FIG. 8 d shows a density plot of Bf at the end of iterations based onthe filter of present invention for cross-section x₁x₃ through thecenter of the grid.

FIG. 8 e shows the results of bright pixel detection using the LTfunction shown in FIG. 8 d.

FIG. 8 f shows the results of bright pixel detection using the LTfunction based on the filter according to the present invention.

FIG. 8 g shows a density plot of Bf at the end of iterations based onthe filter of present invention for cross-section x₂x₃ through thecenter of the grid.

FIG. 8 h shows the results of bright pixel detection using the LTfunction shown in FIG. 8 g.

FIG. 8 i shows the results of bright pixel detection using the LTfunction based on the filter according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Before explaining the disclosed embodiments of the present invention indetail it is to be understood that the invention is not limited in itsapplication to the details of the particular arrangements shown sincethe invention is capable of other embodiments. Also, the terminologyused herein is for the purpose of description and not of limitation.

The methods of the present invention provide an algorithm for tomographyin the motion contaminated case. The motion contaminated case occurswhen the object being scanned is undergoing some transformation duringthe scan. Thus, the phrases “the object is undergoing a transformation”and “there is some motion in the object” have the same meaning in thisinvention.

It is shown that micro locally, away from some critical directions, LTis equivalent to a pseudo differential operator of order one. LT alsoproduces nonlocal artifacts that have the same strength as usefulsingularities. When motion is not accurately known, singularities insidethe object f being scanned spread in different directions. For example,a single edge can become a double edge. In the case of a double edge,the image of f looks cluttered. Based on this observation the presentinvention provides an algorithm for motion estimation using an empiricmeasure of image clutter, referred to throughout the description as edgeentropy. By minimizing edge entropy, the motion model is found. Thealgorithm is flexible and can also be used for solving the misalignmentcorrection problem.

The following detailed description discloses cone-beam LT function Bfand establishes its main properties and then explains the location andstrength of the nonlocal artifacts. As opposed to LT in the static case,it is not possible to find the direction of differentiation, whichreduces the strength of the artifact by one order in the scale ofSobolev spaces. A similar result for a different geometry was recentlyreported in E. T. Quinto, Electron microscope tomography, Conferencetalk at workshop on Mathematical Methods in Emerging Modalities ofMedical Imaging, Banff International Research Station, Banff, Canada,2009. Then, explicit formulas for the shift between the singularities inBf and f in the case when motion is known incorrectly is obtained. Asimilar result is described in A. Katsevich. Improved cone beam localtomography, Inverse Problems, 22 (2006), pp. 627-643 which gives only animplicit relation, and the model used for describing changes in f isdifferent from the model used in the present invention. The novel motionestimation algorithm as well as a description of the motion model anddefinition of edge entropy is described and results of numericalexperiments on motion estimation and misalignment correction are given.

Let C be a smooth curve in

³l

s→z(s)∈

³ ,|z′ _(s)(s)|≠0,  Eq. (1)

-   -   where l⊂        is an interval. Usually the source moves along C with constant        speed, so we identify s with time variable.

Fix any s₀∈l. We refer to s=s₀ as the reference time. To describe themotion inside the object being scanned, we introduce the function ψ.Suppose y=ψ(s,x) is the position of the particle at time s, which islocated at x at the reference time s=s₀. We assume that for each s∈l thefunction ψ(s,x):

³→

³ is a diffeomorphism. Physically this means that two distinct pointscannot move into the same position. This assumption is quite natural,since cardiac motion is not infinitely compressible. The inverse of ψ isthe function x=v(s,y):

³→

³. It gives the original position x of the particle at the referencetime, which is located at y at time s. We assume that both psi and v areidentity maps outside of some open set U, which contains the support ofthe object, and ψ, v∈C^(∞)(l×

³). As usual, we assume that C is at a positive distance from U.Obviously.v(s,ψ(s,x))≡x,ψ(s,v(s,x))≡x.  Eq. (2)

Differentiating the first equation in equation (2) with respect to s andx gives useful identitiesv′ _(s)(s,ψ(s,x))+∇v(s,ψ(s,x))ψ′_(s)(s,x)≡0,∇v(s,ψ(s,x))∇ψ(s,x)≡Id.  Eq.(3)

where Id is the 3×3 identity matrix. In equation (3) and everywherebelow we use the convention that the operator ∇ acts with respect tospace variables. Thus ∇v(s,y)=∇_(y)v(s,u) and ∇ψ(s,x)=∇_(x)ψ(s,x).

Since matter is conserved, the x-ray density at time s and point v isgiven by |∇v(s,y)|f(v(s,y)). Hence the data areD _(f)(s,β):=∫₀ ^(∞) |∇v(s,z(s)+tβ)|f(v(s,z(s)+tβ))dt,s∈l.  Eq. (4)where β runs through a subset of the unit sphere determined by thedetector. The human tissue is not compressible, so in most cases we canassume |∇v(s,y)|≈1 when performing numerical experiments.

The present invention introduces the following LT function:

$\begin{matrix}{{{{{\left( {f} \right)(x)} = {\int_{l}{{\varphi\left( {s,x} \right)}\frac{\partial^{2}}{\partial q^{2}}{D_{f}\left( {s,{\beta\left( {s,{x + {q\;{\Theta\left( {s,x} \right)}}}} \right)}} \right)}}}}}_{q = 0}{\mathbb{d}s}},} & {{Eq}.\mspace{14mu}(5)}\end{matrix}$where

$\begin{matrix}{{{\beta\left( {s,x} \right)} = \frac{{\psi\left( {s,x} \right)} - {z(s)}}{{{\psi\left( {s,x} \right)} - {z(s)}}}},} & {{Eq}.\mspace{14mu}(6)}\end{matrix}$

θ(s,x)):l×U→

³\0 is a smooth function, and φ∈C₀ ^(∞)(l×U). Note that Equation (5)reduces to Equation (2.2) of A. Katsevich, Improved cone beam localtomography, Inverse Problems. 22 (2006), pp. 627-643, if β(s,x+qθ(s,x))is replaced with β(q,x). Equation (2.2) of A. Katsevich, Improved conebeam local tomography, Inverse Problems, 22 (2006), pp. 627-643, wasdeveloped with the goal of reducing the global artifact inherent incone-beam data inversion as much as possible. The additional flexibilityprovided by θ is needed for increasing computational efficiency. Aslight change in the direction of differentiation away from the optimalone may lead to a significant speed-up at the expense of only a slightincrease in the global artifact. The function φ in Equation (5)determines the time interval, which is used for motion estimation.Defineφ(x,s,t):=v(s,z(s)+t(ψ(s,x)−z(s))),t>0,s∈I,x∈U.  Eq. (7)

For a fixed x∈U and s∈l, φ(x,s,t),t>0, is the pre-image of the ray withvertex at z(s) and passing through ψ(s,x). For a fixed x∈U, φ(x,s,t)defines a surface parameterized by s and t. For convenience, thissurface is denoted φ_(x). Using Equation (3), we getφ_(s)(x,s,t):=v′ _(s)(s,{hacek over (y)})′∇v(s,{hacek over (y)})(z′_(s)(s)′t(ψ_(s)(s,x)−z′ _(s)(s)))=∇v(s,{hacek over (y)}){[z′_(s)(s)+t(ψ′_(s)(s,x)−z′ _(s)(s))]−ψ_(s)(s,{hacek over (x)})},   Eq. (8)φ′_(t)(x,s,t):=∇v(s,{hacek over (y)})(ψ(s,x)−z(s)),  Eq. (9)where{hacek over (y)}(x,s,t)=z(s)+t(ψ(s,x)−z(s)),{hacek over (x)}=v(s,{hacekover (y)}).  Eq. (10)

If t≠1, then {hacek over (y)}=(x,s,t)≠ψ(s,x). The expression in bracketsin Equation (8) is the velocity of the point {hacek over (y)}, if weregard it as a fixed point which divides the line segment with endpointsz(s) and ψ(s,x) in the ratio t:1−t. ψ′_(s)(s,{hacek over (x)}) is thevelocity of {hacek over (y)}, if it moves according to the motionfunction ψ. From Equations (8), (9), the surface φ_(x) is smooth at thepoint φ(x,s,t) if the difference of the two velocities is not parallelto the line segment. We say that φ_(x) is smooth if it is smooth at anypoint z∈φ_(x), z≠r.

Proposition 1.

Suppose φ_(x) is smooth for all x∈U. The operator B defined by Equation(5) extends to a map ε′(U)→ε′(U), andWF(Bf)⊂WF _(u)(f)∪E(f,C,ψ),E(f,C,ψ):={(x,η)∈T″U\0:(y,ξ)∈N ⁺φ_(x) ∩WF(f),η=ξ·Εφ(x,s ₀ ,t ₀),y=φ(x,s₀ ,t ₀)≠x,(s ₀ ,x)∈suppφ}.  Eq. (11)

Here N⁺φ_(x) is the co-normal bundle of φ_(x). In short, g has anadditional singularity at x if φ_(x) is tangent to singsupp f at somepointy y≠x. The singularities of Bf, which coincide with those of f, are“useful” (from the point of view of practical applications oftomography), while the set E(f, C, ψ) represents the artifact.

Proof.

Denote

$\begin{matrix}{{{{{{{m:={\inf\limits_{{s \in I},{x \in U}}{{x - {z(s)}}}}},{M:=\sup\limits_{{s \in I},{x \in U}}}}}x} - {z(s)}}},} & {{Eq}.\mspace{14mu}(12)}\end{matrix}$and pick δ, 0<δ<m. Let w(t) be a function with the propertiesw(t)∈C ₀ ^(∞)([m−δ,M+δ]),w(t)=1,t∈[m,M].  Eq. (13)

This function is inserted in the integral in Equation (4) to ensure thatthe integration with respect to t is performed over a compact interval,which does not contain t=0.

Pick any g∈C^(∞)(U) and consider the integral<Bƒ,g>:=∫ _(U)(Bf)(x)g(x)dx,  Eq. (14)where f∈C₀ ^(∞)(U). Substituting Equation (4) into Equation (5) andchanging variablest ₁ +t/|ψ(s,x+qθ(s,x))−z(s)|  Eq. (15)we get that the argument of f in Equation (14) becomesz=v(s,z(s)+t ₁(ψ(s,x+qθ(s,x))−z(s)))  Eq. (16)

Applying Equation (2) gives

$\begin{matrix}{{v\left( {s,{\frac{{\psi\left( {s,z} \right)} - {z(s)}}{t_{1}} + {z(s)}}} \right)} = {x + {q\;{\Theta\left( {s,x} \right)}}}} & {{Eq}.\mspace{14mu}(17)}\end{matrix}$

Since θ(s,x) is a smooth function, q is restricted to a smallneighborhood of zero, and t₁ is bounded away from zero, it is clear thatEquation (17) defines a smooth diffeomorphism z→x=X(z, s, t, q). i)Taking the derivative with respect to q outside the integral in Equation(14). ii) interchanging the order of integration so that the integralwith respect to t becomes the innermost one, and iii) changing variablesx→z according to Equation (17), we get<Bf,g>=<f,B″g>,  Eq. (18)where B″g∈C^(∞)(U). The first assumption of the proposition now follow,from continuity.

The proof Equation (11) is given below.

Next we compute the principal symbol of B. Besides the smoothness ofφ_(x), the additional assumptions we make in this calculation are that(1) φ″_(st)(x,s,t=1) is never a zero vector, and (2) φ″_(st)(x,s,t=1)and φ′_(t)(x,s,t=1) are not parallel. Let us discuss these assumptions.Setting t=1 in Equation (9) and Equation (10) gives {hacek over(y)}=ψ(s,x) and

$\begin{matrix}{{\Phi_{st}^{''}\left( {x,s,{t = 1}} \right)} = {{{\frac{\mathbb{d}}{\mathbb{d}s}\left\lbrack {\nabla{v\left( {s,{\psi\left( {s,x} \right)}} \right)}} \right\rbrack}\left( {{\psi\left( {s,x} \right)} - {z(s)}} \right)} + {{\nabla{v\left( {s,{\psi\left( {s,x} \right)}} \right)}}{\left( {{\psi_{s}^{\prime}\left( {s,x} \right)} - {z_{s}^{\prime}(s)}} \right).}}}} & {{Eq}.\mspace{14mu}(19)}\end{matrix}$

From the second equation in Equation (3),

$\begin{matrix}{{{{{\frac{\mathbb{d}}{\mathbb{d}s}\left\lbrack {\nabla{v\left( {s,{\psi\left( {s,x} \right)}} \right)}} \right\rbrack}{\nabla{\psi\left( {s,x} \right)}}} + {{\nabla{v\left( {s,{\psi\left( {s,x} \right)}} \right)}}{\nabla{\psi_{s}^{\prime}\left( {s,x} \right)}}}} = 0},} & {{Eq}.\mspace{14mu}(20)}\end{matrix}$soφ″_(st)(x,s,t=1)=∇v(s,ψs,ψ(s,x)[(ψ′_(s)(s,x)−z′_(s)(s))−∇ψ′_(s)(s,x)∇v(s,ψ(s,x))(ψ(s,x)−z(s))].  Eq. (21)

Since v is a diffeomorphism, φ″_(st)(x,s,t=1)=0 is equivalent toz′ _(s)(s)=ψ′_(s)(s,x)−∇ψ′(s,x)∇v(s,ψ(s,x))(ψ(s,x)−z(s)).  Eq. (22)

If the source rotates sufficiently fast compared to the motion of themedium. Equation (22) is never satisfied. Comparing Equation (21) withEquation (8) we get that φ″_(st)(x,s,t=1) and φ′_(t)(x,s,t=1) are notparallel ifψ(s,x)−z(s)∥(ψ′_(s)(s,x)−z′_(s)(s))−∇ψ′_(s)(s,x)∇v(s,ψ(s,x))(ψ(s,x)−z(s)).  Eq. (23)

Assuming again that the source rotates sufficiently fast, Equation (23)is equivalent to the requirement that the tangent to the sourcetrajectory never points into the region of interest. This is a commoncondition, which is satisfied by all practical scanning trajectories.

From Equations (4) and (5) we get

( ⁢ f ) ⁢ ( x ) = ∫ ⁢ φ ⁡ ( s , x ) ⁢ w ⁡ ( t ) × ∂ 2 ∂ q 2 ⁢  ∇ v ⁡ ( s , z ⁡ (s ) + t ⁢ ⁢ β ⁡ ( s , x + q ⁢ ⁢ Θ ) ) )  ⁢ f ( v ⁡ ( s , z ⁡ ( s ) + t ⁢ ⁢ β ⁡ ( s, x + q ⁢ ⁢ Θ ) )  q = 0 ⁢ ⅆ t ⁢ ⅆ s . Eq . ⁢ ( 24 )

Representing f in terms of its Fourier transform and changing variablesgives

( ⁢ f ) ⁢ ( x ) = 1 ( 2 ⁢ ⁢ π ) 3 ⁢ ∫ ⁢ f ~ ⁡ ( ξ ) ⁢ B ⁡ ( x , ξ ) ⁢ ⅇ - ⅈ ⁢ ⁢ ξ ·x ⁢ ⅆ ξ , ⁢ B ⁡ ( x , ξ ) := ∫ ⁢ Q 0 ⁡ ( x , ξ , s , t ) ⁢ ⅇ - ⅈ ⁢ ⁢ ξ · ( Φ ⁡ (x , s , t ) - x ) ⁢ ⅆ t ⁢ ⅆ s , Eq . ⁢ ( 25 )where Q₀(x, ξ, s, t)∈S²(U×

³), the seminorms of Q₀ as member of the symbol class S² are uniformlybounded with respect to (s,t)∈

², and the asymptomatics of Q₀ is given byQ _(O)(x,ξ,s,t)=−φ(s,x)w(t|ψ(s,x)−z(s)|)|ψ(s,x)−z(s)|[(tξ·∇v(s,{hacekover (y)}(x,s,t))∇ψ(s,x)θ(s,x))+O(|ξ|],|ξ|→∞.  Eq. (26)

The term O(|ξ|) in Equation (26) is stable when differentiated withrespect to s and t any number of times. Using Equations (8), (9), we getthat at the stationary point of the phaseξ·∇v(s,{hacek over (y)})([z′(s)+t(ψ′_(s)(s,x)−z′(s))]−ψ′_(s)(s,{hacekover (x)})=0,ξ·∇v(s,{hacek over (y)})(ψ(s,x)−z(s))=0.  Eq. (27)

If t=1, then x={hacek over (x)} and the first equation in, Equation (27)is trivially satisfied. Hence (s,t=1) is a stationary point if ξ isperpendicular to ∇v(s,{hacek over (y)})(ψ(s,x)−(z(s)), where {hacek over(y)}=ψ(s,x). By construction, ξ·φ″_(st)(x,s,t=1)≠0. Thus the stationarypoint is non-degenerate if ξ·φ″_(st)(x,s,t=1)≠0. In view of the secondequation in Equation (27), the critical direction at any (s,x) is givenbyξ_(CT)(s,x):=φ′_(t)(x,s,t)|_(t=1)×φ″_(st)(x,s,t)|_(t=1.)  Eq. (28)

Let s_(j)=s_(j)(x,ξ), j=1, 2, . . . be the solutions to Equation (27)with t=1. Assume ξ is away from a conic neighborhood of the setCrit(x):={ξ∈

³\0:ξ=ξ_(CT)(s,x),(s,x)∈suppφ}.  Eq. (29)

Then the critical points (s_(j), t=1) are non-degenerate, and by thestationary phase method

$\begin{matrix}{{{B\left( {x,{\sigma\;\xi}} \right)} = {{{- 2}\pi\;\sigma{\sum\limits_{j}{{\varphi\left( {s_{j},x} \right)}{{{\psi\left( {s_{j},x} \right)} - {y\left( s_{j} \right)}}}\frac{{{\xi \cdot {\Theta\left( {s_{j},x} \right)}}}^{2}}{{\xi \cdot {\Phi_{st}^{''}\left( {x,s_{j},{t = 1}} \right)}}}}}} + {O(1)}}},{\sigma->{\infty.}}} & {{Eq}.\mspace{14mu}(30)}\end{matrix}$

Here we have used the second equation in Equation (3), thatφ(x,s,t=1)≡x, and the signature of the Hessian of the phase at thestationary point equals zero. If we choose, for example,θ(s,x)=φ″_(st)(x,s,t=1),  Eq. (31)

then Equation (30) becomes

$\begin{matrix}{{{B\left( {x,{\sigma\;\xi}} \right)} = {{{- 2}\pi\;\sigma{\sum\limits_{j}{{\varphi\left( {s_{j},x} \right)}{{{\psi\left( {s_{j},x} \right)} - {y\left( s_{j} \right)}}} \times {{\xi \cdot {\Phi_{st}^{''}\left( {x,s_{j},{t = 1}} \right)}}}}}} + {O(1)}}},{\sigma->{\infty.}}} & {{Eq}.\mspace{14mu}(32)}\end{matrix}$

Artifact.

We are now interested in solutions to Equation (27) with t≠1. Similarlyto Equation (25), we have:

$\begin{matrix}{{\left( {f} \right)(x)} = {\frac{1}{\left( {2\;\pi} \right)^{3}}{\int{{\overset{\sim}{f}(\xi)}\left\{ {\int{{Q_{0}\left( {x,\xi,s,t} \right)}{\mathbb{e}}^{{- {\mathbb{i}}}\;{\xi \cdot {\Phi{({x,s,t})}}}}{\mathbb{d}s}{\mathbb{d}t}}} \right\}{{\mathbb{d}\xi}.}}}}} & {{Eq}.\mspace{14mu}(33)}\end{matrix}$

Consider the integral with respect to s and t. Pick some x₀∈U, s₀∈I, andt₀≠1, and setξ₀=φ′_(s)(x ₀ ,s ₀ ,t ₀)×φ′_(t)(x ₀ ,s ₀ ,t ₀),  Eq. (34)

Suppose s=(x,ξ) and t=(x,ξ) solve the systemξ·φ′_(z)(x,s,t)=0,ξ·φ′_(t)(x,s,t)=0  Eq. (35)for (x,ξ) a conic neighborhood of (x₀,ξ₀). In general there can beseveral solutions, but we are looking for the one close to (s₀,t₀).Obviously, s(x,ξ) and t(x,ξ) are homogeneous of degree zero in ξ.

Systems in Equation (35) and Equation (27 are the same. However, sincet≈1, no additional insight is gained by representing φ in terms of ψ andv.

Let κ(x,ξ) be the Gaussian curvature of φ_(x) at the pointy=φ(x,s(x,ξ),t(x,ξ)). When there is no motion, φ_(x) is a ruled surfacewith zero Gaussian curvature. In the presence of motion we can assumethat, generically, κ(x,ξ)≠0. The Hessian of the phase at the stationarypoint is proportional to the curvature:

$\begin{matrix}{{{\det\begin{pmatrix}{\xi \cdot {\Phi_{ss}^{''}\left( {x,s,t} \right)}} & {\xi \cdot {\Phi_{st}^{''}\left( {x,s,t} \right)}} \\{\xi \cdot {\Phi_{st}^{''}\left( {x,s,t} \right)}} & {\xi \cdot {\Phi_{tt}^{''}\left( {x,s,t} \right)}}\end{pmatrix}} = {{\kappa\left( {x,\xi} \right)}{\xi }^{2}{{{\Phi_{s}^{\prime}\left( {x,s,t} \right)} \times {\Phi_{t}^{\prime}\left( {x,s,t} \right)}}}^{2}}},} & {{Eq}.\mspace{14mu}(36)}\end{matrix}$where x, ξ, s, and t satisfy Equation (35). By assumption φ′_(s) andφ′_(t) are linearly independent, i.e. φ_(x) _(O) is smooth aty₀=φ(x₀,s(x₀,ξ₀),t(x₀,ξ₀)). Hence the right-hand side of Equation (36)is not zero x₀, ξ₀, s₀, t₀, the Hessian is non-degenerate, and thefunctions s(x,ξ),t(x,ξ) are locally well-defined and smooth. By thestationary phase method,∫Q ₀(x,ξ,s,t)e ^(−iξ·φ(x,s,t)) dsdt=Q ₁(x,ξ)e ^(−iξ·φ(x,s(x,ξ),t(x,ξ))),  Eq. (37)where Q₁ is a symbol from the class S¹ in a conic neighborhood of(x₀,ξ₀) (cf. (26)), and the asymptotics of Q₁ is given by

$\begin{matrix}{{{Q_{1}\left( {x,\xi} \right)} = {{c\frac{Q_{0}\left( {x,\xi,{s\left( {x,\xi} \right)},{t\left( {x,\xi} \right)}} \right)}{{{\det\left( {\xi \cdot \Phi^{''}} \right)}}^{1/2}}} + {O(1)}}},{{\xi }->\infty},} & {{Eq}.\mspace{14mu}(38)}\end{matrix}$where ξ·φ″ is the matrix in Equation (36), and constant c incorporatessome powers of 2π and i. Combine Equation (33) and Equation (38):

$\begin{matrix}{{{\left( {f} \right)(x)} = {\frac{1}{\left( {2\;\pi} \right)^{3}}{\int{{\overset{\sim}{f}(\xi)}{Q_{1}\left( {x,\xi} \right)}{\mathbb{e}}^{{- {\mathbb{i}}}\;{a{({x,\xi})}}}{\mathbb{d}\xi}}}}},} & {{Eq}.\mspace{14mu}(39)}\end{matrix}$where a(x,ξ):=ξ·φ(x,s(x,ξ),t(x,ξ)). If det(∂²a(x,ξ)/∂x∂ξ)≠0 at (x₀,ξ₀),then locally Equation (39) is a Fourier Integral Operator (FIO)associated with a canonical transformation, and the order of theoperator equals one (see L., Hormander, The Analysis of Linear PartialDifferential Operators. IV. Fourier Integral Operators. Spinger-Verlag,Berlin, 1985, pp. 25, 26). In view of Equation (30) this means that theartifacts and useful singularities can be of the same strength in thescale of Sobolev spaces.

Incorrectly Known Motion.

Suppose that instead of the motion function ψ we know its approximationψ_(∈)(s,x)=ψ(s,x)+∈ψ₁(s,x). In this case the function φ of Equation (7)is replaced byφ(∈x,s,t):=v(s,z(s)+t(ψ_(∈)(s,x)−z(s))).  Eq. (40)

Consequently, the useful singularities of Bf no longer coincide with thesingularities of f. To find the shift between them we assume that theerror in ψ_(∈) is small, i.e., ∈→0, and find the first orderapproximation of the shift. If ∈=0, the function s(x,ξ) is determinedfrom Equation (35) with t(x,ξ)≡1. If ∈≠0, we have to solveξ·φ′_(s)(∈,x _(∈) ,s _(∈) ,t _(∈))=0, ξ·φ′_(t)(∈,x _(∈) ,s _(∈) ,t_(∈))=0,φ(∈,x _(∈) ,s _(∈) ,t _(∈))=φ(0,x,s,t=1).  Eq. (41)

Equations (41) means that the singularity of f at x is mapped into thesingularity of Bf at x_(∈). Set x_(∈)=x+Δx, s_(∈)=s+Δs, , t_(∈)=1+Δt.Expanding Equation (41) in the Taylor series around ∈=0, using Equation(35), and keeping the first order terms in ∈, gives∈ξ·φ″_(∈s)+ξ·∇φ′_(s) Δx+ξ·φ″ _(ss) Δs+ξ·φ″ _(st) Δt=0,∈ξ·φ″_(∈t)+ξ·∇φ′_(t) Δx+ξ·φ″ _(st) Δs+ξ·φ″ _(tt) Δt=0,∈φ′_(∈)+∇φΔx+φ′_(s) Δs+φ′ _(t) Δt=0  Eq. (42)

All the derivatives in Equation (42) are computed at ∈=0,x,s=s(x,ξ), andt=1. Since φA(0,x,s,t=1)≡x, then ∇φ′_(s)=0, φ″_(ss)=0, φ′_(s)=0. Thefirst and third equations in Equation (42) yield:

$\begin{matrix}{{{\Delta\; t} = {{- \varepsilon}\frac{\xi \cdot \Phi_{\varepsilon\; s}^{''}}{\xi \cdot \Phi_{st}^{''}}}},{{\Delta\; x} = {{{\varepsilon\left( {\nabla\Phi} \right)}^{- 1}\left\lbrack {{\Phi_{t}^{\prime}\frac{\xi \cdot \Phi_{\varepsilon\; s}^{''}}{\xi \cdot \Phi_{st}^{''}}} - \Phi_{\varepsilon}^{\prime}} \right\rbrack}.}}} & {{Eq}.\mspace{14mu}(43)}\end{matrix}$When t=1, ∇φ=Id, andφ′_(t) =∇v(ψ(s,x)−z(s)),φ′_(∈) =∇vψ ₁(s,x).  Eq. (44)

Substituting into Equation (43) gives

$\begin{matrix}{{{\Delta\; x} = {{\varepsilon\;{\nabla{{v\left( {s,{\psi\left( {s,x} \right)}} \right)}\left\lbrack {{c\left( {{\psi\left( {s,x} \right)} - {z(s)}} \right)} - {\psi_{1}\left( {s,x} \right)}} \right\rbrack}}} + {O\left( \varepsilon^{2} \right)}}},\mspace{79mu}{c = {\frac{\xi \cdot \Phi_{\varepsilon\; s}^{''}}{\xi \cdot \Phi_{st}^{''}}.}}} & {{Eq}.\mspace{14mu}(45)}\end{matrix}$

By our assumption ξ·φ′_(st)≠0, so Δx is indeed of order O(Å), ∈→0.

End of Proof of Proposition 1.

Using a partition of unity we may suppose that WF(f) is a subset of asufficiently small conic neighborhood of (x₀,ξ₀) ∈T*U, and Q₀≡0 (cf.Equations (26) and (33)) for (s,t) outside a sufficiently smallneighborhood of (s₀, t₀) ∈l×

. Initially we consider the case t₀≠1. First of all, from Equations (4)and (5), (Bf)(x)≡0 outside a neighborhood of y₀=φ(x₀, s₀, t₀). In viewof the partition of unity, Equation (4) needs to be modified byincluding a cut-off function depending on t. Passing to a finerpartition of unity if necessary, Equation (33) implies that (Bf)(x) issmooth near y₀ unless ξ₀ is parallel to φ′_(s)(x₀, s₀, t₀)×φ′_(t)(x₀,s₀, t₀). If the two vectors are parallel, we multiply Equation (33) byφ₁(x)e^(ix·η), where φ₁∈C₀ ^(∞) and is supported in a neighborhood ofy₀, and η₀≠ξ₀·∇φ(x₀, s₀, t₀). Integrating with respect to x and usingthe standard argument (see e.g. Y. V. Egorov and B.-W. Sehulze,Pseudo-differential Operators, Singularities, Applications, Birkhauser,Basel, 1997, p. 114), we get that (y₀,η₀)∉WF(Bf).

Suppose now t₀=1. Since φ(x, s, t=1)≡x and ξ₀=ξ₀=ξ₀·∇φ(x₀, s₀, t₀=1), weget as before that (x₀,ξ₀)∉WF(ƒ) implies (x₀,ξ₀)∉WF(Bf), and Equation(11) is proven.

A Motion Estimation Algorithm.

The motion estimation algorithm of the present invention is base on LT.In the case of static objects, the discontinuities (or, edges) of f andBf generally coincide (see e.g., D. Finch, L-R. Lan, and G. Uhlmann,Microlocal analysis of the x-ray transform with sources on a curve, inInside Out: Inverse Problems and Applications. G. Uhlmann, ed.,Cambridge University Press, Cambridge, UK, 2003, pp. 193-218: A.Katsevich. Improved cone beam local tomography, Inverse Problems, 22(2006), pp. 627-43; A. K. Louis and P. Mass, Contour reconstruction in3-1) X-ray CT, IEEE Trans. Med. Imaging, 12 (1993) pp. 764-769) exceptfor the added singularities or artifacts in Bf and the singularities off that are invisible from the data. As mentioned above, if there is someuncompensated motion in f, the edges or f and Bf no longer coincide.Practically this means that if motion is not known (or, is knownincorrectly) edges in the reconstructed image spread out. A single edgeproduces multiple edges at several locations. Consequently, thereconstructed image looks clattered or random. We can use a measure ofrandomness in the reconstructed image Bf to gauge whether our motionmodel is accurate or not. In what follows we call this measure “edgeentropy”. Using this idea, we summarize the proposed motion estimationalgorithm as follows.

Assume some motion model;

Perform motion-compensated LT image reconstruction using current motionmodel:

Compute edge entropy of the LT image;

If edge entropy is low (i.e., the edges have not spread too much), stop.If edge entropy is high, change the motion model and go to step (2).

A similar idea was used in Y. Kyriakou, R. M. Lapp, L. Hillebrand, D.Ertel, and W. Kalender, Simultaneous misalignment correction forapproximate circular cone-beam computed tomography. Phys. Med. Biol., 53(2008), pp. 6267-6289, for misalignment correction in circular cone beamCT. The main novelty of our approach is that we use LT instead of globalFeldkump type (FDK) reconstruction. On one hand, the use of LT allows usto significantly speed up the iterations. On the other hand, many toolsthat work with conventional images (most notably, image entropy) do notwork with LT images, so we had to develop alternative tools fromscratch. The following paragraphs describe the key steps of thealgorithm in more detail.

Motion Model.

Let [s_(t),s_(r)]⊂l be a parameter/time window, which is used for motionestimation. The center point s₀=(s_(l)+s_(r))/2 is taken as referencetime. The primary purpose of the algorithm of the present invention isto perform local (in time) motion estimation, thus the width of thewindow S:=s_(r)−s_(l) is usually rather short, in our experiments S istypically less than one gantry rotation. Let D⊂U denote the region wheremotion takes place. We assume that D is a rectangle, i.e. D:={(x₁, x₂,x₃)∈

³: L_(k)≦x_(k)≦R_(k), k=1, 2, 3}. To represent motion, we consider aregular grid over D. The grid planes arex _(k)=ζ_(ik):=L_(k) +iΔx _(k),0≦i≦N _(k)+1,k=1,2,3.  Eq. (46)where Δx_(k)=(R_(k)−L_(k))/N_(k)+1) is the step-size along the k-thaxis. Thus, grid in Equation (46) has N₁N₂N₃ interior nodes, and foreach direction k there are N_(k)+2 planes x_(k)=∫_(0k), . . . ,x_(k)=ζ_(N) _(k) _(+1,k). Because of motion, the grid planes deform overtime:x ₁=ζ_(i1) +a _(i1)(s)φ[(x ₂ −L ₂)/(R ₂ −L ₂)]φ[(x ₃ −L ₃)/(R ₃ −L₃)],1≦i≦N ₁,x ₂=ζ_(i2) +a _(i2)(s)φ[(x ₁ −L ₁)/(R ₁ −L ₁)]φ[x ₃ −L ₃)/(R ₃ −L₃)],1≦i≦N ₂,x ₃=ζ_(i3) +a _(i3)(s)φ[(x ₁ −L ₁)/(R ₁ −L ₁)]φ[(x ₂ −L ₂)/(R ₂ −L₂)],1≦i≦N ₃.  Eq. (47)

Each line in Equation (47) defines a separate surface, which correspondsto a deformation of one of the original planes Equation (46). We assumethat motion equals zero at the boundary of D, so the boundary gridplanes (i.e. those given by x_(k)=ζ_(ik), i=0 or N_(k)+1, k=1, 2, 3) donot deform. In Equation (47), the function φ is smooth, defined on theinterval [0,1], and equals zero at both endpoints of the interval. Sincethe time window [s_(l),s_(r)] is sufficiently short, we assume that thefunctions a_(ik)(s) are linear:a _(ik)(s)=a _(ik)(s . . . s ₀)/(0.5S),k=1,2,3.  Eq. (48)where a_(ik), 1≦i≦N_(k), k=1, 2, 3, are constants to be determined. Notethat substituting s=s₀ into Equation (47) gives the rectangular grid ofEquation (46). Equations (47) and (48) allow us to describe motion ofevery point in D. To determine where a node from the original grid ofEquation (46) is located at time s, we identify the three planes wherethe node is located, deform them according to Equation (47), and thenfind the point of intersection of the three resulting surfaces. Locationof all other pixels is computed using trilinear interpolation.

Edge Entropy.

Suppose Bf is computed on a regular grid (x_(i) ₁ , x_(i) ₂ , x_(i) ₃ ),1≦i_(k)≦M_(k), k=1, 2, 3, which covers D. Suppose, for simplicity, thatthe step-size of the grid is the same along every axis and equals Δx.Nodes of the grid are denoted x_(i):=(x_(i) ₁ , x_(i) ₂ , x_(i) ₃ ),where l=(i₁, i₂, i₃). Of course, this grid should be much more densethan the one in Equation (46) (M_(k)>>N_(k)). We also need a shiftedgrid with nodes x _(l):=( x _(i) ₁ , x _(i) ₂ , x _(i) ₃ ), where x _(i)_(k) =x_(i) _(k) +Δx/2, 1≦i_(k)≦M_(k)−1, k=1, 2, 3. Introduce thedistance function:dist( x _(I) , x _(J))=max(|i ₁ −j ₁ |,|i ₂ −j ₂ |,i ₃ −j ₃|).  Eq. (49)

Calculation of edge entropy consists of several steps. Let parameter κ,0<κ<1, be fixed.

Using finite differences, compute the norm of the gradient at the nodesof the shifted grid |∇(Bf)( x _(l))|;

Compute the empirical histogram of the norm of the gradient;

Using the histogram, estimate the value M such that |∇(Bf)( x _(l))|Mfor 100κ percent of the points (such points are called “bright”);

4. By running a sliding window over the image compute the total numberof points x ₁ whose distance (in the sense of Equation (49)) to theclosest bright point equals either 2, 3, or 4;

Divide this number by the total number of nodes in the grid and multiplyby 100) (to get percents). The result is the edge entropy of the imageBf.

Numerical Experiments.

The original phantom is a superposition of seven balls as shown FIGS.1-3. FIGS. 1 a, 1 b and 1 c show x₁x₂-cross-sections of the phantom at atime corresponding to 101, 900 and 501, respectively. FIGS. 2 a, 2 b and2 c show x₁x₃-cross-sections of the phantom at a time corresponding to101, 900 and 501, respectively, respectively. FIGS. 3 a, 3 b and 3 cshow x₂x₃-cross-sections of the phantom at a time corresponding to 101,900 and 501, respectively, respectively.

The motion of the medium is described by the function

$\begin{matrix}{{\psi\left( {s,x} \right)} = {x + \left\{ {{\begin{matrix}{{25{\cos\left( {0.35\left( {s - s_{0}} \right)} \right)}\Theta},} & {{{x} < 10},} \\{{25{\cos\left( {0.35\left( {s - s_{0}} \right)} \right)}\frac{75 - {x}}{65}\Theta},} & {{10 \leq {x} < 75},} \\{0,} & {{{x} \geq 75};}\end{matrix}\mspace{79mu}\Theta} = {\left( {{\cos\;\theta_{2}\cos\;\theta_{1}},{\cos\;\theta_{2}\sin\;\theta_{1}},{\sin\;\theta_{2}}} \right).}} \right.}} & {{Eq}.\mspace{14mu}(50)}\end{matrix}$

Here s₀ is reference time, θ₁=70°, θ₂=30°. In this section the units oflength are always mm. The detector array is curved and passes throughthe isocenter. Pixel size on the detector is 0.5 along columns, and 10⁻³radians along rows. The source trajectory is circular: x₁=R cos s, x₂=Rsin s, x₃=0, and the source to isocenter distance is R=600. There are1000 projections per one rotation, 0≦s<2π. The time corresponding toprojection 501 was chosen as reference time s₀=π.

For motion estimation we used only the data corresponding to the rangeof projections. The data are simulated using ray tracing as described,e.g., in A. Katsevich. Motion compensated local tomography, InverseProblems, 24 (2008), 045013. Following the common practice in medicalimaging, the changes in density were not tracked due to motion (S. Rit,D. Sarrut, and L. Desbat, Comparison of analytic and algebraic methodsfor motion compensated cone-beam CT reconstruction of the thorax. IEEETrans. Med. Imaging, 28 (2009), pp. 1513-1525). This is equivalent tosetting |∇v|=1 in Equation (4).

The function Bf is computed on the 112×112×112 regular grid covering thecube −75≦x_(k)≦75, k=1, 2, 3. To make the resulting algorithm asnumerically efficient as possible, we use the simplest version of themotion compensated LT. To this end the derivative ∂²/∂q² in Equation (5)is replaced by the second derivative of the cone beam data alongdetector rows.

Let D_(l) be a box-like region bounded by six neighboring planesEquation (46). As is easily seen, the values of Bf(x) for all x∈D_(l)depend only on the six parameters describing the deformation of the sixplanes that form its boundary. Minimization of edge entropy uses thisobservation and is done using the following approach.

Step 1. Let a_(ik), i=1, . . . N_(k), k=1, 2, 3, be the current bestestimate of the motion parameters. Let some Δa≠0 be given. Pick one ofthe D_(l)'s. Let a_(i) ₁ _(k) ₁ , . . . a_(i) ₆ _(k) ₆ be the sixparameters affecting the chosen region. Compute 3⁶ subimages Bf(x),x∈D_(l), corresponding to the sets ã_(i) ₁ _(k) ₁ , . . . , ã_(i) ₆ _(k)₆ , where each ã_(ik) equals either a_(ik), or a_(ik)−Δa, or a_(ik)+Δa.Store all the subimages on the disk, and repeat for all D_(l)'s.

Step 2. Run the loop over all 3^(N) ¹ ^(+N) ² ^(+N) ³ sets a_(ik), i=1 .. . N_(k), k=1, 2, 3, where each ã_(ik) equals either a_(ik), ora_(ik)−Δa, or a_(ik)+Δa. This is done by reading the appropriatesubimages from the disk and combining them into a single image of Bf(v),x∈D. Then compute edge entropy for the obtained image. From the 3^(N) ¹^(+N) ² ^(+N) ³ sets of parameters find the one which produces the imagewith the smallest entropy.

Steps 1 and 2 constitute a single iteration. The initial values ofa_(ik) are chosen to be zero (which is the no motion assumption). Thevalue of Δa is chosen from some a priori considerations. After the endof each iteration, the optimal set of parameters identified at Step 2 ispassed on to Step 1. Also, the value of Δa is decreased. In ourexperiments we used Δa_(initial)=10, Δa_(new)=0.75Δa_(old), and threeiterations were performed.

Results of experiments are shown in FIGS. 4 and 5. FIG. 4 shows theinitial image of Bf computed under the (incorrect) assumption of nomotion. FIG. 5 shows the final image of Bf computed for the motionmodel, that was determined by the algorithm. In these experiments weused N₁=N₂=N₃=4 (cf. (47)). We used κ=0.0125 to compute edge entropy. Atthe beginning of iterations (FIG. 4) the value of entropy is 9.81%, andat the end −8.40% (FIG. 5).

To illustrate another application of the present invention we use it forsolving a misalignment correction problem in the case of a distortedcircular scan. Suppose that under the ideal circumstances the sourcetrajectory is a circle. Suppose that because of mechanical instabilitiesthe actual trajectory is a distorted circle given by

$\begin{matrix}{{x_{1} = {R\;\cos\; s}},{x_{2} = {R\;\sin\; s}},{x_{3} = {\sum\limits_{n = 1}^{N}{c_{n}{{\cos({ns})}.}}}}} & {{Eq}.\mspace{14mu}(51)}\end{matrix}$

Here c_(n), n=1, 2, . . . , N, are unknown and are to be determined fromthe tomographic data. Suppose, for simplicity, that the detector alwayscontains the x₃-axis, its center has the same x₃-coordinate as thesource, and is perpendicular to the source to center line. Thus, thedetector moves along the x₃-axis in the same way as the source. It isclear that, similarly to the motion contaminated case, if sourcetrajectory is known with error, the edges spread and the image looksmore random. Consequently, the procedure outlined earlier applies hereas well (with the optimization of the “motion model” replaced by theoptimization of the “trajectory model”). In our numerical experiment weused the same seven-ball phantom as before (only it is not moving now),and took N=5 with c₁=c₃=c₅=5, c₂=c₄=−5. Other parameters: source toisocenter distance R and size of reconstruction grid are the same as inthe first experiment. As initial approximation all c_(i)'s were taken tobe zero. Optimization was done using the Nelder-Mead simplex algorithm,see J. Nelder and R. Mead, A simplex method for function minimization.Comput. J., 7 (1965), pp. 308-313. At the end of iterations the computedconstants are 3.58, −5.87, 4.81, −5.36, 4.52. Initial entropy was equal20.1%, and at the end of iterations it was 8.44%. See FIG. 6 for thedensity plots of Bf.

In the case of noise-free for low noise) data, the algorithm based on LTas outlined above works well. If data are sufficiently noisy, theproposed scheme is unstable. Indeed, to compute the LT function wedifferentiate the data two times. Then, to find bright pixels, wedifferentiate the image one more time. To make the algorithm more robustobserve that instead of ∂²/∂q² Equation (5) we can use almost any evenconvolution kernel which preserves singular support. It was foundempirically that convolving the tomographic data along data rows withdie kernel, whose frequency characteristic is given by |λ|^(0.2)produces good results. In FIG. 7, left two columns, we see the LT imagesand the results of bright pixel detection using the filter ∂²/∂q² inEquation (5) in the case of (erroneous assumption) of zero motion in thedata. The right column of FIG. 7 shows the results of bright pixeldetection using the new filter. As is seen, the latter is much lessnoisy than the former. In FIG. 8 we see the reconstructions at the endof iterative motion estimation. Left column shows the intermediate“local” tomography image, middle column shows the results of brightpixel detection. Note that these images are computed from the noisy datain the process of iterations and correspond to the motion model with theleast edge entropy. Even though the filter is no longer local, we keepusing the name “local tomography” to emphasize the fact that we still donot reconstruct density values (as opposed to conventional “globaltomography”). To better illustrate our results we computed thetraditional LT function from the noise-free data using the computedmotion model, see the right panel. It is clearly seen from these imagesthat the edges are now much less spread than they were at the beginningof iterations.

Discussion. In this patent application. LT in the motion contaminatedcase was studied. It is shown that microlocally, away from same criticaldirections, LT is equivalent to a pseudo-differential operator of orderone. LT also produces non-local artifacts that are of the same strengthas useful singularities. As opposed to the static case, here it is notpossible to choose the direction of differentiation to reduce thestrength of the artifact by one order in the scale of Sobolev spaces. Onthe other hand, if motion is sufficiently small, it is expected thatchoosing θ as in Equation (31) which is analogous to what was done in A.Katsevich. Improved cone beam local tomography, Inverse Problems, 22(2006), pp. 627-643 (compare Equation (32) with Equation (2.11) inKatsevich (2006)), may help reduce the artifacts. Then we consider thecase when motion is not accurately known. It is shown that when asingularity is seen from two different source positions, it spreads indifferent directions. A single edge becomes a double edge. Based on thisobservation we propose an algorithm for motion estimation. The algorithmis quite flexible and is used for solving the misalignment correctionproblem.

While the invention has been described, disclosed, illustrated and shownin various terms of certain embodiments or modifications which it haspresumed in practice, the scope of the invention is not intended to be,nor should it be deemed to be, limited thereby and such othermodifications or embodiments as may be suggested by the teachings hereinare particularly reserved especially as they fall within the breadth andscope of the claims here appended.

What is claimed is:
 1. A method for estimating scan parameters fromtomographic data, the method comprising: collecting tomographic data ofan object during a scan, wherein one or more parameters of the scan arenot accurately known; reconstructing an image of the object, wherein theinformation about the object in the image useful for estimating the scanparameters pertains only to one or more edges of the object image, theedges defined as sharp spatial features inside the object; estimatingthe one or more scan parameters using the reconstructed image of theobject; and repeating the reconstructing of the image of the object andthe estimating of the one or more scan parameters two or more times toimprove the estimate of the one or more scan parameters, wherein thereconstructing of the image of the object and the estimating of the oneor more scan parameters are repeated until a number of edges of theobject having a predetermined characteristic are below a preselectedthreshold.
 2. The method of claim 1, wherein estimating the one or morescan parameters using the reconstructed image of the object furthercomprises, analyzing a spatial distribution of the edges of the objectin the reconstructed image of the object.
 3. The method of claim 2,wherein analyzing a spatial distribution of the edges of the object inthe reconstructed image of the object is based on a prevalence of doubleedges in the reconstructed image, wherein double edges are defined asedges located close to each other.
 4. The method of claim 3, whereinreconstructing an image of the object further comprises: assuming one ormore values for the one or more parameters of the scan that are notaccurately known; and using the assumed one or more values during thereconstructing of the image of the object.
 5. The method of claim 4,further comprising: making the conclusion that the assumed values of theparameters are not close to the actual values of the parameters when alarge number of double edges are detected; and making the conclusionthat the assumed values of the parameters are close to the actual valuesof the parameters when a small number of double edges are detected.